The present invention relates generally to the field of data analytics, and more particularly to applying predictive analytics to electromagnetic noise signal data to predict user touch events.
Predictive analytics is an area of data mining that deals with extracting information from data and using the information to predict trends and behavior patterns. Often the unknown event of interest is in the future, but predictive analytics can be applied to any type of unknown, whether it be in the past, present or future. Predictive analytics encompasses a variety of statistical techniques from modeling, machine learning, and data mining that analyze current and historical facts to make predictions about future, or otherwise unknown events. The core of predictive analytics relies on determining relationships between explanatory variables and predictive variables from past occurrences, and exploiting them to predict a future event.
Electromagnetic (EM) noise signal detection is the detection of the EM noise that a product produces or captures from nearby electronic and electromechanical objects. Electronic and electromechanical objects commonly emit EM noise during operation. Non-electronic and non-electromechanical objects, such as large structural objects like doors, window frames, and furniture, may also have unique EM noise signals by acting as antennas that capture and propagate EM noise from nearby electronic and electromechanical devices. Objects emitting or conducting EM noise can have unique signal characteristics, making it possible to differentiate one object from another. EM noise signal emission may be intentional, such as in cell phones, or unintentional, such as in power lines. In response to a user touching an EM noise signal emitting or conducting object, EM noise signals are conducted through the human body, which also acts as an antenna. The conducted EM noise signals can be detected by a radio receiver.